Upload MiniMaxAI_MiniMax-M2_1.py with huggingface_hub

MiniMaxAI_MiniMax-M2_1.py

@@ -11,24 +11,14 @@
 # ///
 
 try:
-    # Load model directly
-    from transformers import AutoTokenizer, AutoModelForCausalLM
+    # Use a pipeline as a high-level helper
+    from transformers import pipeline
     
-    tokenizer = AutoTokenizer.from_pretrained("MiniMaxAI/MiniMax-M2")
-    model = AutoModelForCausalLM.from_pretrained("MiniMaxAI/MiniMax-M2")
+    pipe = pipeline("text-generation", model="MiniMaxAI/MiniMax-M2")
     messages = [
         {"role": "user", "content": "Who are you?"},
     ]
-    inputs = tokenizer.apply_chat_template(
-    	messages,
-    	add_generation_prompt=True,
-    	tokenize=True,
-    	return_dict=True,
-    	return_tensors="pt",
-    ).to(model.device)
-    
-    outputs = model.generate(**inputs, max_new_tokens=40)
-    print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
+    pipe(messages)
     with open('MiniMaxAI_MiniMax-M2_1.txt', 'w', encoding='utf-8') as f:
         f.write('Everything was good in MiniMaxAI_MiniMax-M2_1.txt')
 except Exception as e:
@@ -43,24 +33,14 @@ except Exception as e:
     with open('MiniMaxAI_MiniMax-M2_1.txt', 'a', encoding='utf-8') as f:
         import traceback
         f.write('''```CODE: 
-# Load model directly
-from transformers import AutoTokenizer, AutoModelForCausalLM
+# Use a pipeline as a high-level helper
+from transformers import pipeline
 
-tokenizer = AutoTokenizer.from_pretrained("MiniMaxAI/MiniMax-M2")
-model = AutoModelForCausalLM.from_pretrained("MiniMaxAI/MiniMax-M2")
+pipe = pipeline("text-generation", model="MiniMaxAI/MiniMax-M2")
 messages = [
     {"role": "user", "content": "Who are you?"},
 ]
-inputs = tokenizer.apply_chat_template(
-	messages,
-	add_generation_prompt=True,
-	tokenize=True,
-	return_dict=True,
-	return_tensors="pt",
-).to(model.device)
-
-outputs = model.generate(**inputs, max_new_tokens=40)
-print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
+pipe(messages)
 ```
 
 ERROR: